Andrew Gelman

Orcid: 0000-0002-6975-2601

Affiliations:
  • University of Columbia, New York, USA


According to our database1, Andrew Gelman authored at least 42 papers between 1988 and 2024.

Collaborative distances:
  • Dijkstra number2 of four.
  • Erdős number3 of four.

Timeline

Legend:

Book 
In proceedings 
Article 
PhD thesis 
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Other 

Links

Online presence:

On csauthors.net:

Bibliography

2024
Statistics as a Social Activity: Attitudes toward Amalgamating Evidence.
Entropy, August, 2024

Bayesian workflow for time-varying transmission in stratified compartmental infectious disease transmission models.
PLoS Comput. Biol., 2024

Pareto Smoothed Importance Sampling.
J. Mach. Learn. Res., 2024

2023
Bayesian spatial modelling of localised SARS-CoV-2 transmission through mobility networks across England.
PLoS Comput. Biol., November, 2023

Prediction scoring of data-driven discoveries for reproducible research.
Stat. Comput., 2023

Artificial Intelligence and Aesthetic Judgment.
CoRR, 2023

Federated Learning as Variational Inference: A Scalable Expectation Propagation Approach.
Proceedings of the Eleventh International Conference on Learning Representations, 2023

2022
Pathfinder: Parallel quasi-Newton variational inference.
J. Mach. Learn. Res., 2022

Stacking for Non-mixing Bayesian Computations: The Curse and Blessing of Multimodal Posteriors.
J. Mach. Learn. Res., 2022

A fast regression via SVD and marginalization.
Comput. Stat., 2022

Delivering data differently.
CoRR, 2022

The Worst of Both Worlds: A Comparative Analysis of Errors in Learning from Data in Psychology and Machine Learning.
Proceedings of the AIES '22: AAAI/ACM Conference on AI, Ethics, and Society, Oxford, United Kingdom, May 19, 2022

2021
Reviews.
Am. Math. Mon., 2021

Accounting for uncertainty during a pandemic.
Patterns, 2021

Toward a Taxonomy of Trust for Probabilistic Machine Learning.
CoRR, 2021

To design interfaces for exploratory data analysis, we need theories of graphical inference.
CoRR, 2021

Bayesian hierarchical stacking.
CoRR, 2021

2020
Expectation Propagation as a Way of Life: A Framework for Bayesian Inference on Partitioned Data.
J. Mach. Learn. Res., 2020

A Fast Linear Regression via SVD and Marginalization.
CoRR, 2020

2018
Yes, but Did It Work?: Evaluating Variational Inference.
Proceedings of the 35th International Conference on Machine Learning, 2018

2017
Erratum to: Practical Bayesian model evaluation using leave-one-out cross-validation and WAIC.
Stat. Comput., 2017

Practical Bayesian model evaluation using leave-one-out cross-validation and WAIC.
Stat. Comput., 2017

Automatic Differentiation Variational Inference.
J. Mach. Learn. Res., 2017

The Prior Can Often Only Be Understood in the Context of the Likelihood.
Entropy, 2017

A Safe Depth Forecasting Model for Insuring Tubewell Installations Against Arsenic Risk in Bangladesh.
Proceedings of the Computational Science and Its Applications - ICCSA 2017, 2017

The statistical significance filter leads to overconfident expectations of replicability.
Proceedings of the 39th Annual Meeting of the Cognitive Science Society, 2017

2016
Centralized Analysis of Local Data, with Dollars and Lives on the Line: Lessons from the Home Radon Experience.
Proceedings of the Computational Social Science: Discovery and Prediction, 2016

2015
Simulation-efficient shortest probability intervals.
Stat. Comput., 2015

Automatic Variational Inference in Stan.
Proceedings of the Advances in Neural Information Processing Systems 28: Annual Conference on Neural Information Processing Systems 2015, 2015

2014
Understanding predictive information criteria for Bayesian models.
Stat. Comput., 2014

The No-U-turn sampler: adaptively setting path lengths in Hamiltonian Monte Carlo.
J. Mach. Learn. Res., 2014

The Mythical Swing Voter.
CoRR, 2014

2012
In praise of the referee
CoRR, 2012

2009
Commentary - Discussion of the Article "Website Morphing".
Mark. Sci., 2009

2007
Weighted Classical Variogram Estimation for Data With Clustering.
Technometrics, 2007

Manipulating and summarizing posterior simulations using random variable objects.
Stat. Comput., 2007

2006
Bayesian Measures of Explained Variance and Pooling in Multilevel (Hierarchical) Models.
Technometrics, 2006

Multilevel (Hierarchical) Modeling: What It Can and Cannot Do.
Technometrics, 2006

An experimental study of storable votes.
Games Econ. Behav., 2006

2000
Bayesian probabilistic extensions of a deterministic classification model.
Comput. Stat., 2000

Type S error rates for classical and Bayesian single and multiple comparison procedures.
Comput. Stat., 2000

1988
FRM: An Intelligent Assistant for Financial Resource Management.
Proceedings of the 7th National Conference on Artificial Intelligence, 1988


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